Establishing a multimodal personality for a multimodal application in dependence upon attributes of user interaction
Establishing a multimodal personality for a multimodal application, including evaluating, by the multimodal application, attributes of a user's interaction with the multimodal application; selecting, by the multimodal application, a visual demeanor in dependence upon the values of the attributes of the user's interaction with the multimodal application; and incorporating, by the multimodal application, the visual demeanor into the multimodal application.
Latest Nuance Communications, Inc. Patents:
- INTERACTIVE VOICE RESPONSE SYSTEMS HAVING IMAGE ANALYSIS
- GESTURAL PROMPTING BASED ON CONVERSATIONAL ARTIFICIAL INTELLIGENCE
- SPEECH DIALOG SYSTEM AND RECIPIROCITY ENFORCED NEURAL RELATIVE TRANSFER FUNCTION ESTIMATOR
- Automated clinical documentation system and method
- CROSS-ATTENTION BETWEEN SPARSE EXTERNAL FEATURES AND CONTEXTUAL WORD EMBEDDINGS TO IMPROVE TEXT CLASSIFICATION
This application is a continuation of and claims the benefit under 35 U.S.C. §120 of U.S. patent application Ser. No. 13/748,441, which was filed in the U.S. Patent and Trademark Office on Jan. 23, 2013, which is herein incorporated by reference in its entirety. U.S. patent application Ser. No. 13,748,441 is a continuation of and claims the benefit under 35 U.S.C. §120 of U.S. patent application Ser. No. 11/530,628, which was filed in the U.S. Patent and Trademark Office on Sep. 11, 2006, and which is herein incorporated by reference in its entirety.
BACKGROUND OF THE INVENTION1. Field of the Invention
The field of the invention is data processing, or, more specifically, methods, apparatus, and products for establishing a multimodal personality for a multimodal application.
2. Description of Related Art
User interaction with applications running on small devices through a keyboard or stylus has become increasingly limited and cumbersome as those devices have become increasingly smaller. In particular, small handheld devices like mobile phones and PDAs serve many functions and contain sufficient processing power to support user interaction through other modes, such as multimodal access. Devices which support multimodal access combine multiple user input modes or channels in the same interaction allowing a user to interact with the applications on the device simultaneously through multiple input modes or channels. The methods of input include speech recognition, keyboard, touch screen, stylus, mouse, handwriting, and others. Multimodal input often makes using a small device easier.
Multimodal applications often run on servers that serve up multimodal web pages for display on a multimodal browser. A ‘multimodal browser,’ as the term is used in this specification, generally means a web browser capable of receiving multimodal input and interacting with users with multimodal output. Multimodal browsers typically render web pages written in XHTML+Voice (‘X+V’). X+V provides a markup language that enables users to interact with an multimodal application often running on a server through spoken dialog in addition to traditional means of input such as keyboard strokes and mouse pointer action. Visual markup tells a multimodal browser what the user interface is to took like and how the user interface is to behave when the user types, points, or clicks. Similarly, voice markup tells a multimodal browser what to do when the user speaks to it. For visual markup, the multimodal browser uses a graphics engine; for voice markup, the multimodal browser uses a speech engine. X+V adds spoken interaction to standard web content by integrating XHTML (eXtensible Hypertext Markup Language) and speech recognition vocabularies supported by VoiceXML. For visual markup, X+V includes the XHTML standard. For voice markup, X+V includes a subset of VoiceXML. For synchronizing the VoiceXML elements with corresponding visual interface elements, X+V uses events. XHTML includes voice modules that support speech synthesis, speech dialogs, command and control, and speech grammars. Voice handlers can be attached to XHTML elements and respond to specific events. Voice interaction features are integrated with XHTML and can consequently be used directly within XHTML content.
In addition to X+V, multimodal applications also may be implemented with Speech Application Tags (‘SALT’). SALT is a markup language developed by the Salt Forum. Both X+V and SALT are markup languages for creating applications that use voice input/speech recognition and voice output/speech synthesis. Both SALT applications and X+V applications use underlying speech recognition and synthesis technologies or ‘speech engines’ to do the work of recognizing and generating human speech. As markup languages, both X+V and SALT provide markup-based programming environments for using speech engines in an application's user interface. Both languages have language elements, markup tags, that specify what the speech-recognition engine should listen for and what the synthesis engine should ‘say.’ Whereas X+V combines XHTML, VoiceXML, and the XML Events standard to create multimodal applications, SALT does not provide a standard visual markup language or eventing model. Rather, it is a low-level set of tags for specifying voice interaction that can be embedded into other environments. In addition to X+V and SALT, multimodal applications may be implemented in Java with a Java speech framework, in C++, for example, and with other technologies and in other environments as well.
Current lightweight voice solutions require a developer to build a grammar and lexicon to limit the potential number of words that an automated speech recognition (‘ASR’) engine must recognize—as a means for increasing accuracy. Pervasive devices have limited interaction and input modalities due to the form factor of the device, and kiosk devices have limited interaction and input modalities by design. In both cases the use of speaker independent voice recognition is implemented to enhance the user experience and interaction with the device. The state of the art in speaker independent recognition allows for some sophisticated voice applications to be written as long as there is a limited vocabulary associated with each potential voice command. For example, if the user is prompted to speak the name of a city the system can, with a decent level of confidence, recognize the name of the city spoken. In the case where there is no explicit context, such as a blank text field for inputting any search query, this speaker independent recognition fails because a reasonably sized vocabulary is not available.
Incorporating speech into multimodal application, however, naturally leads users to expect or at least wish that the multimodal application would have some personality. Personality is characterized by dynamism, however, and in the current state of the art, the user interface, page after page, voice after voice, is static. Despite providing additional modes for user interaction, web applications today do not dynamically adjust to meet the user's rising expectation of speed and interaction quality.
SUMMARY OF THE INVENTIONMethods, apparatus, and computer program products are described for enabling developers of multimodal applications to portray more likeable technical features to the end user. A multimodal application according to embodiments of the present invention may interact quickly and starkly with a teenage user who is savvy with multimodal devices. The same multimodal application a few moments later may interact slowly and forgivingly with an elderly user who is not device-savvy. Mulitmodal personalities composed of vocal demeanors define how the multimodal application acts. More particularly, methods, apparatus, and computer program products are described for establishing a multimodal personality for a multimodal application that include evaluating, by the multimodal application, attributes of a user's interaction with the multimodal application; selecting, by the multimodal application, a vocal demeanor in dependence upon the values of the attributes of the user's interaction with the multimodal application; and incorporating, by the multimodal application, the vocal demeanor into the multimodal application.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.
Exemplary methods, apparatus, and products for establishing a multimodal personality for a multimodal application according to embodiments of the present invention are described with reference to the accompanying drawings, beginning with
A multimodal device is an automated device, that is, automated computing machinery or a computer program running on an automated device, that is capable of accepting from users more than one mode of input, keyboard, mouse, stylus, and so on, including speech input—and also displaying more than one mode of output, graphic, speech, and so on. A multimodal device is generally capable of accepting speech input from a user, digitizing the speech, and providing digitized speech to a speech engine for recognition. A multimodal device may be implemented, for example, as a voice-enabled browser on a laptop, a voice browser on a telephone handset, an online game implemented with Java on a personal computer, and with other combinations of hardware and software as may occur to those of skill in the art. Because multimodal applications may be implemented in markup languages (X+V, SALT), object-oriented languages (Java, C++), procedural languages (the C programming language), and in other kinds of computer languages as may occur to those of skill in the art, this specification uses the term ‘multimodal application’ to refer to any software application, server-oriented or client-oriented, thin client or thick client, that administers more than one mode of input and more than one mode of output, typically including visual and speech modes.
The system of
-
- personal computer (107) which is coupled for data communications to data communications network (100) through wireline connection (120),
- personal digital assistant (‘PDA’) (112) which is coupled for data communications to data communications network (100) through wireless connection (114),
- mobile telephone (110) which is coupled for data communications to data communications network (100) through wireless connection (116), and
- laptop computer (126) which is coupled for data communications to data communications network (100) through wireless connection (118).
Each of the example multimodal devices (152) in the system of
-
- RTP Payload Format for European Telecommunications Standards Institute (ETSI) European Standard ES 201 108 Distributed Speech Recognition Encoding
and the Internet Draft entitled - RTP Payload Formats for European Telecommunications Standards institute (ETSI) European Standard ES 202 050, ES 202 211, and ES 202 212 Distributed Speech Recognition Encoding,
the FETE provides standard RTP payload formats for various codecs. It is useful to note, therefore, that there is no limitation in the present invention regarding codecs, payload formats, or packet structures. Speech for establishing a multimodal personality for a multimodal application according to embodiments of the present invention may be encoded with any codec, including, for example: - AMR (Adaptive Multi-Rate Speech coder)
- ARDOR (Adaptive Rate-Distortion Optimized sound codeR),
- Dolby Digital (A/52, AC3),
- DTS (DTS Coherent Acoustics),
- MP1 (MPEG audio layer-1),
- MP2 (MPEG audio layer-2) Layer 2 audio codec (MPEG-1, MPEG-2 and non-ISO MPEG-2.5),
- MP3 (MPEG audio layer-3) Layer 3 audio codec (MPEG-1, MPEG-2 and non-ISO MPEG-2.5),
- Perceptual Audio Coding,
- FS-1015 (LPC-10),
- FS-1016 (CELP),
- G.726 (ADPCM),
- G.728 (LD-CELP),
- G.729 (CS-ACELP),
- GSM,
- HILN (MPEG-4 Parametric audio coding), and
- others as may occur to those of skill in the art.
- RTP Payload Format for European Telecommunications Standards Institute (ETSI) European Standard ES 201 108 Distributed Speech Recognition Encoding
As mentioned, a multimodal device according to embodiments of the present invention, is capable of providing speech to a speech engine for recognition. A speech engine is a functional module, typically a software module, although it may include specialized hardware also, that does the work of recognizing and generating or ‘synthesizing’ human speech. The speech engine implements speech recognition by use of a further module referred to in this specification as a ASR engine, and the speech engine carries out speech synthesis by use of a further module referred to in this specification as a text-to-speech (‘TTS’) engine. As shown in
Each of the example multimodal devices (152) in the system of
The use of these four example multimodal devices (152) is for explanation only, not for limitation of the invention. Any automated computing machinery capable of accepting speech from a user, providing the speech digitized to an ASR engine, and receiving and playing speech prompts and responses from the voice server may be improved to function as a multimodal device for establishing a multimodal personality for a multimodal application according to embodiments of the present invention.
The system of
The system of
-
- a link layer with the Ethernet™ Protocol or the Wireless Ethernet™ Protocol,
- a data communications network layer with the Internet Protocol (‘IP’),
- a transport layer with the Transmission Control Protocol (‘TCP’) or the User Datagram Protocol (‘UDP’),
- an application layer with the HyperText Transfer Protocol (‘HTTP’), the Session Initiation Protocol (‘SIP’), the Real Time Protocol (‘RTP’), the Distributed Multimodal Synchronization Protocol (‘DMSP’), the Wireless Access Protocol (‘WAP’), the Handheld Device Transfer Protocol (‘HDTP’), the ITU protocol known as H.323, and
- other protocols as will occur to those of skill in the art.
The system of
The arrangement of the voice server (151), the multimodal devices (152), and the data communications network (100) making up the exemplary system illustrated in
Establishing a multimodal personality for a multimodal application according to embodiments of the present invention in a thin client architecture typically is implemented with one or more voice servers, computers, that is, automated computing machinery, that provide speech recognition and speech synthesis. For further explanation, therefore,
Stored in RAM (168) is a multimodal server application (188), a module of computer program instructions capable of operating a voice server in a system that is configured to establish a multimodal personality for a multimodal application according to embodiments of the present invention. Multimodal server application (188) provides voice recognition services for multimodal devices by accepting requests for speech recognition and returning speech recognition results, including text representing recognized speech, text for use as variable values in dialogs, and text as string representations of scripts for semantic interpretation. Multimodal server application (188) also includes computer program instructions that provide text-to-speech (‘TTS’) conversion for voice prompts and voice responses to user input in multimodal applications such as, for example, X+V applications or Java Speech applications. Multimodal server application (188) in this example is also configured to establish a multimodal personality for a multimodal application according to embodiments of the present invention by evaluating, by the multimodal application, attributes of a user's interaction with the multimodal application; selecting, by the multimodal application, a vocal demeanor in dependence upon the values of the attributes of the user's interaction with the multimodal application; and incorporating, by the multimodal application, the vocal demeanor into the multimodal application.
The multimodal server application (188) in this example is configured to establish a multimodal personality for a multimodal application according to embodiments of the present invention by evaluating attributes (504) of a user's interaction with the multimodal application, selecting a vocal demeanor (550) in dependence upon the values of the attributes of the user's interaction with the multimodal application, and incorporating the vocal demeanor into the multimodal application. The multimodal server application (188) in this example is configured to evaluate attributes of a user's interaction with the multimodal application by measuring or otherwise deriving values for the attributes (504). The multimodal application in this example may be viewed as a combination of the multimodal server application and a remote multimodal device application cooperating through a VOIP protocol to evaluate attributes of user interaction. The multimodal device application provides user input in the form of digitized speech which the multimodal server application can analyze for pitch, pitch range, richness, voice family, and so on. The multimodal device application can be configured to measure attributes of user interaction that are more appropriately measured on the client side, user response time to prompts, for example, or tracking of the modality of user response, that is, how often the user responds with speech, keyboard, or mouse. The multimodal device application can provide client-side measurement to the multimodal server application through messages of a VOIP protocol. The multimodal server application can statefidly maintain during a VOIP session as computer data the evaluations of attributes of user interaction and use them to select a vocal demeanor as a component of a multimodal personality.
The multimodal server application (188) can incorporate a selected vocal demeanor as a component of a multimodal personality into the multimodal server application by linking one or more markup elements of a markup document of the multimodal server application to one or more styles of a Cascading Style Sheet (‘CSS’) (514) and providing the CSS to a requesting multimodal device application that in turn loads the CSS into a multimodal device application and uses the CSS to control a multimodal user interface, the voice aspects of a multimodal user interface. The multimodal device application, located on a multimodal device across a network from the voice server, is the so-called ‘thin client,’ so-called because much of the functionality for establishing the multimodal personality is implemented on the voice server rather than on the multimodal device.
Cascading Style Sheets is a stylesheet language used to describe the presentation of a document written in a markup language. The common application of CSS is to style web pages written in HTML and XHTML, but the language can be applied to any kind of XML document, including Scalable Vector Graphics (“SVG”) and XML User Interface Language (“XUL”). The CSS specifications are maintained by the World Wide Web Consortium (“W3C”). CSS can control the vocal display of an X+V page as well as the visual display. The aural rendering of a document, already commonly used by the blind and print-impaired communities, combines speech synthesis and “auditory icons.” Often such aural presentation occurs by converting the document to plain text and feeding this to a screen reader—software or hardware that simply reads all the characters on the screen. This results in less effective presentation than would be the case if the document structure were retained. Style sheet properties for aural presentation may be used together with visual properties (mixed media or multimodal) or as an aural alternative to visual presentation. When using aural properties, the aural CSS canvas consists of a three-dimensional physical space (sound surrounds) and a temporal space (one may specify sounds before, during, and after other sounds). The CSS properties also allow authors to vary the quality of synthesized speech (voice type, frequency, inflection, etc.). Here are examples of vocal rules or styles of an aural CSS:
These examples direct a speech synthesizer (TTS engine) to speak headers in a voice (a kind of “audio font”) called “paul,” on a flat tone, but in a very rich voice. Before speaking the headers, a sound sample will be played from the given URL. Paragraphs with class “heidi” will appear to come from front left (if the sound system is capable of spatial audio), and paragraphs of class “peter” from the right. Paragraphs with class “goat” will be rendered very softly.
Multimodal server application (188) in this example is a user-level, multimodal, server-side computer program that may be implemented with a set of VoiceXML documents which taken together comprise a VoiceXML application. Multimodal server application (188) may be implemented as a web server, implemented in Java, C++, or another language, that supports X+V, SALT, or another multimodal language, by providing responses to HTTP requests from X+V, SALT or other multimodal clients. Multimodal server application (188) may, for a further example, be implemented as a Java server that runs on a Java Virtual Machine (102) and supports a Java voice framework by providing responses to HTTP requests from Java client applications running on multimodal devices. And multimodal server applications that support establishing a multimodal personality for a multimodal application may be implemented in other ways as may occur to those of skill in the art, and all such ways are well within the scope of the present invention.
The voice server in this example includes a speech engine (153). The speech engine is a functional module, typically a software module, although it may include specialized hardware also, that does the work of recognizing and generating human speech. The speech engine (153) includes an automated speech recognition (‘ASR’) engine for speech recognition and a text-to-speech (‘TTS’) engine for generating speech. The speech engine also includes a grammar (104), a lexicon (106), and a language-specific acoustic model (108). The language-specific acoustic model (108) is a data structure, a table or database, for example, that associates SFVs with phonemes representing, to the extent that it is practically feasible to do so, all pronunciations of all the words in a human language. The lexicon (106) is an association of words in text form with phonemes representing pronunciations of each word; the lexicon effectively identifies words that are capable of recognition by an ASR engine.
The grammar (104) communicates to the ASR engine (150) the words and sequences of words that currently may be recognized. For precise understanding, distinguish the purpose of the grammar and the purpose of the lexicon. The lexicon associates with phonemes all the words that the ASR engine can recognize. The grammar communicates the words currently eligible for recognition. The set of words currently eligible for recognition and the set of words capable of recognition may or may not be the same.
Grammars for use in establishing a multimodal personality for a multimodal application according to embodiments of the present invention may be expressed in any format supported by any ASR engine, including, for example, the Java Speech Grammar Format (‘JSGF’), the format of the W3C Speech Recognition Grammar Specification (‘SRGS’), the Augmented Backus-Naur Format (‘ABNF’) from the IETF's RFC2234, in the form of a stochastic grammar as described in the W3C's Stochastic Language Models (N-Gram) Specification, and in other grammar formats as may occur to those of skill in the art. Grammars typically operate as elements of dialogs, such as, for example, a VoiceXML <menu> or an X+V<form>. A grammar's definition may be expressed in-line in a dialog. Or the grammar may be implemented externally in a separate grammar document and referenced from with a dialog with a URI. Here is an example of a grammar expressed in JSFG:
In this example, the elements named <command>, <name>, and <when> are rules of the grammar. Rules are a combination of a rulename and an expansion of a rule that advises an ASR engine which words presently can be recognized. In this example, expansion includes conjunction and disjunction, and the vertical bars ‘|’ mean ‘or.’ An ASR engine processes the rules in sequence, first <command>, then <name>, then <when>. The <command> rule accepts for recognition ‘call’ or ‘phone’ or ‘telephone’ plus, that is, in conjunction with, whatever is returned from the <name> rule and the <when> rule. The <name> rule accepts ‘bob’ or ‘martha’ or ‘joe’ or ‘pete’ or ‘chris’ or ‘john’ or ‘artoush’, and the <when> rule accepts ‘today’ or ‘this afternoon’ or ‘tomorrow’ or ‘next week.’ The command grammar as a whole accepts utterances like these, for example:
-
- “phone bob next week,”
- “telephone martha this afternoon,”
- “remind me to call chris tomorrow,” and
- “remind me to phone pete today.”
The multimodal server application (188) in this example is configured to receive, from a multimodal client located remotely across a network from the voice server, digitized speech for recognition from a user and pass the speech along to the ASR engine (150) for recognition. ASR engine (150) is a module of computer program instructions, also stored in RAM in this example. In carrying out automated speech recognition, the ASR engine receives speech for recognition in the form of at least one digitized word and uses frequency components of the digitized word to derive a Speech Feature Vector (‘SFV’). SFV may be defined, for example, by the first twelve or thirteen Fourier or frequency domain components of a sample of digitized speech. The ASR engine can use the SFV to infer phonemes for the word from the language-specific acoustic model (108). The ASR engine then uses the phonemes to find the word in the lexicon (106).
Also stored in RAM is a VoiceXML interpreter (192), a module of computer program instructions that processes VoiceXML grammars. VoiceXML input to VoiceXML interpreter (192) may originate from VoiceXML clients running remotely on multimodal devices, from X+V clients running remotely on multimodal devices, or from Java client applications running remotely on multimedia devices. In this example, VoiceXML interpreter (192) interprets and executes VoiceXML segments received from remote multimedia clients and provided to VoiceXML interpreter (192) through multimodal server application (188). Also stored in RAM (168) is a Text To Speech (‘TTS’) Engine (194), a module of computer program instructions that accepts text as input and returns the same text in the form of digitally encoded speech, for use in providing speech as prompts for and responses to users of multimodal systems.
Also stored in RAM (168) is an operating system (154). Operating systems useful in voice servers according to embodiments of the present invention include UNIX™, Linux™, Microsoft NT™, AIX™, IBM's i5/OS™, and others as will occur to those of skill in the art. Operating system (154), multimodal server application (188), VoiceXML interpreter (192), ASR engine (150), JVM (102), and TTS Engine (194) in the example of
Voice server (151) of
Voice server (151) of
The example voice server of
The exemplary voice server (151) of
For further explanation,
In addition to the multimodal sever application (188), the voice server (151) also has installed upon it a speech engine (153) with an ASR engine (150), a grammar (104), a lexicon (106), a language-specific acoustic model (108), and a TTS engine (194), as well as a JVM (102), and a Voice XML interpreter (192). VoiceXML interpreter (192) interprets and executes VoiceXML grammars received from the multimodal device application and provided to VoiceXML interpreter (192) through multimodal server application (188). VoiceXML input to VoiceXML interpreter (192) may originate from the multimodal device application (195) implemented as a VoiceXML client running remotely the multimodal device (152), from the multimodal device application (195) implemented as an X+V client running remotely on the multimodal device (152). As noted above, the multimedia device application (195) also may be implemented as a Java client application running remotely on the multimedia device (152), a SALT application running remotely on the multimedia device (152), and in other ways as may occur to those of skill in the art.
VOIP stands for ‘Voice Over Internet Protocol,’ a generic term for routing speech over an IP-based data communications network. The speech data flows over a general-purpose packet-switched data communications network, instead of traditional dedicated, circuit-switched voice transmission lines. Protocols used to carry voice signals over the IP data communications network are commonly referred to as ‘Voice over IP’ or ‘VOIP’ protocols. VOIP traffic may be deployed on any IP data communications network, including data communications networks lacking a connection to the rest of the Internet, for instance on a private building-wide local area data communications network or ‘LAN.’
Many protocols are used to effect VOIP. The two most popular types of VOIP are effected with the IETF's Session Initiation Protocol (‘SIP’) and the ITU's protocol known as ‘H.323.’ SIP clients use TCP and UDP port 5060 to connect to SIP servers. SIP itself is used to set up and tear down calls for speech transmission. VOIP with SIP then uses RTP for transmitting the actual encoded speech. Similarly, H.323 is an umbrella recommendation from the standards branch of the International Telecommunications Union that defines protocols to provide audio-visual communication sessions on any packet data communications network.
The apparatus of
Multimodal server application (188) provides voice recognition services for multimodal devices by accepting requests for speech recognition and returning speech recognition results, including text representing recognized speech, text for use as variable values in dialogs, and text as string representations of scripts for semantic interpretation. Multimodal server application (188) includes computer program instructions that provide text-to-speech (‘TTS’) conversion for voice prompts and voice responses to user input in multimodal applications such as, for example, X+V applications, SALT applications, or Java Speech applications.
The multimodal server application (188) receives speech for recognition from a user and passes the speech through API calls to an ASR engine (150) for recognition. The ASR engine receives digitized speech for recognition, uses frequency components of the digitized speech to derive an SFV, uses the SFV to infer phonemes for the word from the language-specific acoustic model (108), and uses the phonemes to find the speech in the lexicon (106). The ASR engine then compares speech founds as words in the lexicon to words in a grammar to deter mine whether words or phrases in speech are recognized by the ASR engine.
The multimodal server application (188) in this example, in a similar manner to that described above with reference to the system of
The multimodal application in this example may be viewed as a combination of the multimodal server application (188) and a remote multimodal device application (195) cooperating through a VOIP protocol connection (216) to evaluate attributes of user interaction. The multimodal device application (195) provides user input in the form of digitized speech which the multimodal server application (188) can analyze for pitch, pitch range, richness, voice family, and so on. The multimodal device application (195) can be configured to measure attributes of user interaction that are more appropriately measured on the client side, user response time to prompts, for example, or tracking of the modality of user response, that is, how often the user responds with speech, keyboard, or mouse. The multimodal device application (195) can provide client-side measurement to the multimodal server application through messages of a VOIP protocol. The multimodal server application can statefully maintain during a VOIP session as computer data the evaluations of attributes of user interaction and use them to select a vocal demeanor as a component of a multimodal personality.
The multimodal server application (188) can incorporate a selected vocal demeanor (550) as a component of a multimodal personality into the multimodal server application by linking one or more markup elements of a markup document of the multimodal server application to one or more styles of a Cascading Style Sheet (‘CSS’) (514) and providing the CSS to a requesting multimodal device application (195) that in turn loads the CSS into the multimodal device application (195) and uses the CSS to control a multimodal user interface, that is, the voice aspects of a multimodal user interface.
Establishing a multimodal personality for a multimodal application according to embodiments of the present invention in thick client architectures is generally implemented with multimodal devices, that is, automated computing machinery or computers. In the system of
The example multimodal device (152) of
Also stored in RAM (168) in this example is a multimodal device application (195), a module of computer program instructions capable of operating a multimodal device as an apparatus that supports establishing a multimodal personality for a multimodal application according to embodiments of the present invention. The multimodal device application (195) implements speech recognition by accepting speech for recognition from a user and sending the speech for recognition through API calls to the ASR engine (150). The multimodal device application (195) implements generally by sending words to be used as prompts for a user to the TTS engine (194). As an example of thick client architecture, the multimodal device application (195) in this example does not send speech for recognition across a network to a voice server for recognition, and the multimodal device application (195) in this example does not receive synthesized speech, TTS prompts and responses, across a network from a voice server. All grammar processing, voice recognition, and text to speech conversion in this example is performed in an embedded fashion in the multimodal device (152) itself.
More particularly, multimodal device application (195) in this example is a user-level, multimodal, client-side computer program that provides a speech interface through which a user may provide oral speech for recognition through microphone (176), have the speech digitized through an audio amplifier (185) and a coder/decoder (‘codec’) (183) of a sound card (174) and provide the digitized speech for recognition to ASR engine (150). The multimodal device application (195) may be implemented as a set or sequence of X+V documents executing in a multimodal browser (196) or microbrowser that passes VoiceXML grammars and digitized speech through API calls directly to an embedded VoiceXML interpreter (192) for processing. The embedded VoiceXML interpreter (192) may in turn issue requests for speech recognition through API calls directly to the embedded ASR engine (150). Multimodal device application (195) also can provide speech synthesis, TTS conversion, by API calls to the embedded TTS engine (194) for voice prompts and voice responses to user input.
In a further class of exemplary embodiments, the multimodal device application (195) may be implemented as a Java voice application that executes on Java Virtual Machine (102) and calls the ASR engine (150) and the TTS engine (194) directly through APIs for speech recognition and speech synthesis services. In further exemplary embodiments, the multimodal device application (195) may be implemented as a set or sequence of SALT documents executed on a multimodal browser (196) or microbrowser that calls the ASR engine (150) and the TTS engine (194) through APIs for speech recognition and speech synthesis services. In addition to X+V, SALT, and Java implementations, multimodal device application (195) may be implemented in other technologies as will occur to those of skill in the art, and all such implementations are well within the scope of the present invention.
The multimodal device application (195) in this example is configured to establish a multimodal personality for a multimodal application according to embodiments of the present invention by evaluating attributes (504) of a user's interaction with the multimodal application, selecting a vocal demeanor (550) in dependence upon the values of the attributes of the user's interaction with the multimodal application, and incorporating the vocal demeanor into the multimodal application. The multimodal device application (195) in this example is configured to evaluate attributes of a user's interaction with the multimodal application by measuring or otherwise deriving values for the attributes.
The multimodal device application (195) receives user input in the form of digitized speech through the sound card (174) which the multimodal device application (195) can analyze for pitch, pitch range, richness, voice family, and so on. The multimodal device application (195) in this example is configured also to measure other attributes of user interaction such as user response time to prompts, for example, or tracking of the modality of user response, that is, how often the user responds with speech, keyboard, or mouse. The multimodal device application can maintain as computer data the evaluations of attributes of user interaction and use them to select a vocal demeanor as a component of a multimodal personality.
The multimodal device application (195) can incorporate a selected vocal demeanor (550) as a component of a multimodal personality into the multimodal server application by linking one or more markup elements of a markup document of the multimodal server application to one or more styles of a Cascading Style Sheet (‘CSS’) (514), loading the CSS into the multimodal device application (195), and using the CSS to control a multimodal user interface, that is, the voice aspects of a multimodal user interface. The multimodal device application in this example, running on a stand-alone multimodal device with no network, no VOIP connection, and no voice server containing a remote speech engine and a remote multimodal server application, is the so-called ‘thick client,’ so-called because all of the functionality for establishing the multimodal personality is implemented on the multimodal device itself.
For further explanation,
The method of
Interaction mode is the user's chosen mode of providing input through a user interface and responding to prompts for input. Interaction modes may be vocal or non-vocal. Examples of interaction modes include keyboard, mouse movements, and speech. User response rate may be measured as the time elapsed between presentation of prompt by the multimodal application and a user's response to the prompt. Prompts may be vocal or visual.
Speech rate may be measured as the user's speaking rate in words per minute. Voice family in effect measures a font for speech, describing the overall nature and timbre of a voice either in generic terms, male, female, child, or in specific terms, Mary's voice, Jack's voice, and so on. The name of a voice family may be considered in effect to identify a vocal font for a vocal demeanor. Pitch is a measure of the average frequency of a user's speaking voice. Voice family may be inferred from pitch: An average pitch for a male voice is typically about 120 Hertz, while an average pitch for a female voice is typically about 210 Hertz. A measure of voice family therefore is a name of a voice family, from an available or supported set of voice families, whose overall timbre is most closely related to that of a user: a female voice for female user, a male voice for a male user, and so on.
Pitch range is a measure of variation in average pitch. The perceived pitch of a human voice is determined by its fundamental frequency and typically has a value of about 120 Hz for a male voice and about 210 Hz for a female voice. Human languages are spoken with varying inflection and pitch—variations that convey additional meaning and emphasis. A highly animated voice, that is, a voice that is heavily inflected, displays a high pitch range. The measure of pitch range specifies the range over which these variations occur, that is, how much the fundamental frequency deviates from the average pitch. Pitch range therefore may be measured in Hertz. The value of pitch range is a measure of the amount of inflection in a vocal utterance. A low pitch range value indicates a user speaking in a monotone; a high pitch range value identifies an animated voice. A medium pitch range value indicates normal inflection.
Richness is a measure of the brightness of a user's speaking voice. A rich voice will ‘carry’ in a large room, a smooth voice will not. The term ‘smooth’ refers to how a wave form of the voice looks when drawn. A rich voice has a higher ratio of peak amplitude values to average amplitude values than a smooth voice. A rich voice is more ‘condensed’ in effect than a smooth voice.
The method of
The selected vocal demeanor (550) is selected from among a plurality (540) of predefined vocal demeanors. The predefined vocal demeanors may be implemented as computer data structures having data elements representing vocal characteristics, lists of computer data, objects instantiated from demeanor classes in an object-oriented programming language, records in tables of a database, and so on. Selecting a vocal demeanor means selecting a predefined demeanor whose vocal characteristics match the evaluated attributes (504) of user interaction with the multimodal application. A vocal demeanor and the attributes (504) of user interaction with the multimodal application ‘match’ according to embodiments of the present invention when each is characterized by similar attributes. The match is required to be merely ‘similar,’ not exact. ‘Similar’ means identical within some predefined margin of error. A failure to find for selection a vocal demeanor that matches the attributes of user interaction typically results in a multimodal application's use of a default vocal demeanor to formulate its multimodal personality.
The vocal demeanor is not necessarily the only component of a multimodal personality. A multimodal personality may include visual aspects, implemented for example with a visual demeanor, as well. Visual demeanor is the overall visual appearance of a multimodal application, background colors, text colors, text fonts, selection and placement of graphic elements, and so on. Visual demeanor may be characterized by attributes such as age (vibrant colors for young users, quieter colors for mature users), gender (sans serif fonts for women, serifs for men), location (Eiffel. Tower background for Parisians, the Alamo for Texans), time (bright color palettes in the morning, quieter palettes in the evening), application domain (more text for legal subjects, more graphics for architectural subjects), and so on.
The method of
In this example X+V page, a VoiceXML form identified as “drinkform” voice enables an XHTML input form named “fid.” The table data field named “in1” registers “drinkform” as an event handler for “focus” events in the field; that is, when field “in1” gains focus, the multimodal application calls “drinkform” to administer vocal input to field “in1.” By use of the <drinks> grammar:
-
- <drinks>=coffee|tea|milk|nothing;
“drinkform” can recognize the words “coffee,” “tea,” “milk,” or “nothing” as vocal input to field “in1.”
- <drinks>=coffee|tea|milk|nothing;
This example X+V page shows a link, defined as a <link> element, to an external CSS identified by the URL “http://www.ibmcom/style/demeanor.jsp”:
This example X+V page defines a multimodal speech dialog as a VoiceXML <vxml:form> element with id=“drinkform.” The <vxml:form> element includes a prompt <vxml:prompt src=“#p1”> that refers to an <h2> heading element:
identified as id=“p1.” The <h2> heading element is controlled by a class attribute, class=“server,” that identifies the style to be returned from the reference to the external CSS, “demeanor.jsp.” The value of the style returned in this example is:
signifying that the spoken prompt for the <h2> heading is to be rendered in a female voice, and any prompts for <h3> headings are to be rendered in a male voice. Specific demeanor attributes may be implemented as session attributes, or as attributes that persist across sessions in a persistent user profile. Session-specific attributes may be passed as a cookie in the header of an HTTP request for the CSS. Analogous schemes as may occur to those of skill in the art can be constructed for the generation of grammars and the vocabulary used in prompts.
The fact that the referenced CSS is named “demeanor.jsp” indicates that the external CSS is returned from the computation of a Java Server Page. This effectively makes the referenced external CSS a variable. The multimodal application, through its operating environment, a browser or a JVM, can select and return a CSS whose styles effect the selected vocal demeanor. The vocal demeanor typically is selected to match evaluated attributes of user interaction, as described in more detail above. A CSS can be selected from among many, hundreds or thousands, according to the characteristics of a matching demeanor, age, gender, location, application domain, speech rate, voice family, pitch, pitch range, richness, and so on. Returning a selected CSS, loading it into the multimodal application, and using it to govern the presentation of the user interface, graphic and speech aspects in particular, is an example of an effective way of incorporating into the multimodal application a vocal demeanor as a component of multimodal personality.
For further explanation,
In view of the explanations set forth above in this paper, readers will recognize that establishing a multimodal personality for multimodal application according to embodiments of the present invention provides the technical tools to enable developers to provide personalities for multimodal applications that dynamically adapt to the characteristics of a particular user—including the particular attributes of the user's interactions with the multimodal application. By selecting vocal demeanors in dependence upon attributes of user interaction, a user is empowered to interact with a multimodal personality that in effect ‘fits’ the user's own approach to the computer, stow for slow users, fast for fast users, animated speech for animated speakers, calm speech for calm speakers, a personality of similar age and gender as the user, and so on, and so on.
Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for establishing a multimodal personality for a multimodal application. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed on signal bearing media for use with any suitable data processing system. Such signal bearing media may be transmission media or recordable media for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of recordable media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Examples of transmission media include telephone data communications networks for voice communications and digital data communications data communications networks such as, for example, Ethernets™ and data communications networks that communicate with the Internet Protocol and the World Wide Web. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a program product. Persons skilled in the art will recognize immediately that, although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention.
It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims.
Claims
1. A method of operating a multimodal application to tailor a multimodal personality presented by the multimodal application to a user of the multimodal application, the multimodal application comprising a visual interface and a speech interface, the method comprising:
- evaluating speech data of speech of the user, received via the speech interface, to determine at least one attribute of the user's speech; and
- selecting a selected visual demeanor for the multimodal application based at least in part on the determined at least one attribute of the user's speech, wherein the multimodal application can be configured to employ any of a plurality of visual demeanors, wherein the selecting comprises selecting the selected visual demeanor from among the plurality of visual demeanors, each visual demeanor of the plurality of visual demeanors identifying a manner in which to format content for presentation in the visual interface of the multimodal application, each one of the plurality of visual demeanors specifying one or more formatting characteristics to be applied to the content when the one of the plurality of visual demeanors is selected, the one or more formatting characteristics selected from the group consisting of a color in which to present at least some of the content presented in the visual interface, a font in which to present at least some of the content presented in the visual interface, one or more supplemental graphic elements to be added to the content presented in the visual interface, and positions in the visual interface at which to present the content and/or the one or more supplemental graphic elements.
2. The method of claim 1, wherein:
- evaluating the speech data to determine the at least one determined attribute comprises determining a gender of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting the selected visual demeanor based on the gender of the user.
3. The method of claim 2, wherein selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies a formatting characteristic that is a particular font and/or type of font to be used in presenting content to users having the gender.
4. The method of claim 1, wherein:
- evaluating the speech data to determine the at least one determined attribute comprises determining an accent of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies a formatting characteristic that is a particular graphical element to be presented to users having the accent.
5. The method of claim 1, wherein:
- at least some of the plurality of visual demeanors specify formatting characteristics relating to sets of colors to be used in presenting the content in the visual interface; and
- selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies one or more formatting characteristics relating to a set of colors based at least in part on the at least one attribute.
6. The method of claim 1, wherein:
- each visual demeanor of the plurality of visual demeanors is associated with at least one associated attribute; and
- selecting the selected visual demeanor from the plurality of visual demeanors based on the at least one attribute comprises identifying one or more visual demeanors for which at least some of the at least one associated attribute matches the at least one attribute determined from the speech data of speech of the user.
7. At least one non-transitory computer-readable storage medium having encoded thereon executable instructions that, when executed by at least one processor, cause the at least one processor to carry out a method of operating a multimodal application to tailor a multimodal personality presented by the multimodal application to a user of the multimodal application, the multimodal application comprising a visual interface and a speech interface, the method comprising:
- evaluating speech data of speech of the user, received via the speech interface, to determine at least one attribute of the user's speech; and
- selecting a selected visual demeanor for the multimodal application based at least in part on the determined at least one attribute of the user's speech, wherein the multimodal application can be configured to employ any of a plurality of visual demeanors, wherein the selecting comprises selecting the selected visual demeanor from among the plurality of visual demeanors, each visual demeanor of the plurality of visual demeanors identifying a manner in which to format content for presentation in the visual interface of the multimodal application, each one of the plurality of visual demeanors specifying one or more formatting characteristics to be applied to the content when the one of the plurality of visual demeanors is selected, the one or more formatting characteristics selected from the group consisting of a color in which to present at least some of the content presented in the visual interface, a font in which to present at least some of the content presented in the visual interface, one or more supplemental graphic elements to be added to the content presented in the visual interface, and positions in the visual interface at which to present the content and/or the one or more supplemental graphic elements.
8. The at least one non-transitory computer-readable storage medium of claim 7, wherein:
- evaluating the speech data to determine the at least one determined attribute comprises determining a gender of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting the selected visual demeanor based on the gender of the user.
9. The at least one non-transitory computer-readable storage medium of claim 8, wherein selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies a formatting characteristic that is a particular font and/or type of font to be used in presenting content to users having the gender.
10. The at least one non-transitory computer-readable storage medium of claim 7, wherein:
- evaluating the speech data to determine the at least one determined attribute comprises determining an accent of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies a formatting characteristic that is a particular graphical element to be presented to users having the accent.
11. The at least one non-transitory computer-readable storage medium of claim 7, wherein:
- at least some of the plurality of visual demeanors specify formatting characteristics relating to sets of colors to be used in presenting the content in the visual interface; and
- selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies one or more formatting characteristics relating to a set of colors based at least in part on the at least one attribute.
12. The at least one non-transitory computer-readable storage medium of claim 7, wherein:
- the plurality of visual demeanors identify manners in which to present same content via the visual interface; and
- the method further comprises: presenting the same content to the user in the selected visual demeanor via the visual interface; evaluating second speech data of speech of a second user, received via the speech interface, to determine at least one second attribute of the second user's speech; selecting a second selected visual demeanor for the multimodal application based at least in part on the at least one second attribute of the second user's speech; and presenting the same content to the second user in the second selected visual demeanor via the visual interface.
13. An apparatus comprising:
- at least one processor; and
- at least one non-transitory computer-readable storage medium having encoded thereon executable instructions that, when executed by at least one processor, cause the at least one processor to carry out a method of operating a multimodal application to tailor a multimodal personality presented by the multimodal application to a user of the multimodal application, the multimodal application comprising a visual interface and a speech interface, the method comprising: evaluating speech data of speech of the user, received via the speech interface, to determine at least one attribute of the user's speech; and selecting a selected visual demeanor for the multimodal application based at least in part on the determined at least one attribute of the user's speech, wherein the multimodal application can be configured to employ any of a plurality of visual demeanors, wherein the selecting comprises selecting the selected visual demeanor from among the plurality of visual demeanors, each visual demeanor of the plurality of visual demeanors identifying a manner in which to format content for presentation in the visual interface of the multimodal application, each one of the plurality of visual demeanors specifying one or more formatting characteristics to be applied to the content when the one of the plurality of visual demeanors is selected, the one or more formatting characteristics selected from the group consisting of a color in which to present at least some of the content presented in the visual interface, a font in which to present at least some of the content presented in the visual interface, one or more supplemental graphic elements to be added to the content presented in the visual interface, and positions in the visual interface at which to present the content and/or the one or more supplemental graphic elements.
14. The apparatus of claim 13, wherein:
- evaluating the speech data to determine the at least one determined attribute comprises determining a gender of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting the selected visual demeanor based on the gender of the user.
15. The apparatus of claim 14, wherein selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies a formatting characteristic that is a particular font and/or type of font to be used in presenting content to users having the gender.
16. The apparatus of claim 13, wherein:
- evaluating the speech data to determine the at least one determined attribute comprises determining an accent of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies a formatting characteristic that is a particular graphical element to be presented to users having the accent.
17. The apparatus of claim 13, wherein:
- at least some of the plurality of visual demeanors specify formatting characteristics relating to sets of colors to be used in presenting the content in the visual interface; and
- selecting the selected visual demeanor comprises selecting one of the plurality of visual demeanors that specifies one or more formatting characteristics relating to a set of colors based at least in part on the at least one attribute.
18. The apparatus of claim 13, wherein:
- evaluating the speech data to determine the at least one attribute comprises determining an age or age range of the user based at least in part on the speech data; and
- selecting the selected visual demeanor comprises selecting a visual demeanor that specifies a set of colors to be used in the visual interface for users having the age or age range.
19. The apparatus of claim 13, wherein:
- each visual demeanor of the plurality of visual demeanors is associated with at least one associated attribute; and
- selecting the selected visual demeanor from the plurality of visual demeanors based on the at least one attribute comprises identifying one or more visual demeanors for which at least some of the at least one associated attribute matches the at least one attribute determined from the speech data of speech of the user.
20. The apparatus of claim 13, wherein the at least one attribute of the user's speech is unrelated to words used by the user in the speech.
| 5406492 | April 11, 1995 | Suzuki |
| 5577165 | November 19, 1996 | Takebayashi et al. |
| 5584052 | December 10, 1996 | Gulau et al. |
| 5966691 | October 12, 1999 | Kibre et al. |
| 5969717 | October 19, 1999 | Ikemoto |
| 6144938 | November 7, 2000 | Surace et al. |
| 6208972 | March 27, 2001 | Grant et al. |
| 6212502 | April 3, 2001 | Ball et al. |
| 6243375 | June 5, 2001 | Speicher |
| 6275806 | August 14, 2001 | Pertrushin |
| 6301560 | October 9, 2001 | Masters |
| 6334103 | December 25, 2001 | Surace et al. |
| 6404438 | June 11, 2002 | Hatlelid |
| 6513011 | January 28, 2003 | Uwakubo |
| 6606599 | August 12, 2003 | Grant et al. |
| 6708153 | March 16, 2004 | Brittan et al. |
| 6728679 | April 27, 2004 | Strubbe et al. |
| 6856960 | February 15, 2005 | Dragosh et al. |
| 6920425 | July 19, 2005 | Will et al. |
| 6999930 | February 14, 2006 | Roberts et al. |
| 7035805 | April 25, 2006 | Miller |
| 7050977 | May 23, 2006 | Bennett |
| 7171243 | January 30, 2007 | Watanabe et al. |
| 7188067 | March 6, 2007 | Grant et al. |
| 7330890 | February 12, 2008 | Partovi et al. |
| 7349527 | March 25, 2008 | Yacoub et al. |
| 7376586 | May 20, 2008 | Partovi et al. |
| 7398209 | July 8, 2008 | Kennewick et al. |
| 7487085 | February 3, 2009 | Ativanichayaphong et al. |
| 7509659 | March 24, 2009 | McArdle |
| 20020065944 | May 30, 2002 | Hickey et al. |
| 20020092019 | July 11, 2002 | Marcus |
| 20020095295 | July 18, 2002 | Cohen et al. |
| 20020099553 | July 25, 2002 | Brittan et al. |
| 20020120554 | August 29, 2002 | Vega |
| 20020135618 | September 26, 2002 | Maes et al. |
| 20020147593 | October 10, 2002 | Lewis et al. |
| 20020184610 | December 5, 2002 | Chong et al. |
| 20030033152 | February 13, 2003 | Cameron |
| 20030039341 | February 27, 2003 | Burg et al. |
| 20030046316 | March 6, 2003 | Gergic et al. |
| 20030046346 | March 6, 2003 | Mumick et al. |
| 20030101451 | May 29, 2003 | Bentolila et al. |
| 20030125945 | July 3, 2003 | Doyle |
| 20030179865 | September 25, 2003 | Stillman et al. |
| 20030182622 | September 25, 2003 | Sibal et al. |
| 20030195739 | October 16, 2003 | Washio |
| 20030217161 | November 20, 2003 | Balasuriya |
| 20030229900 | December 11, 2003 | Reisman |
| 20030235282 | December 25, 2003 | Sichelman et al. |
| 20040019487 | January 29, 2004 | Kleindienst et al. |
| 20040025115 | February 5, 2004 | Sienel et al. |
| 20040031058 | February 12, 2004 | Reisman |
| 20040044516 | March 4, 2004 | Kennewick et al. |
| 20040049390 | March 11, 2004 | Brittan et al. |
| 20040049391 | March 11, 2004 | Polanyi |
| 20040059705 | March 25, 2004 | Wittke et al. |
| 20040083109 | April 29, 2004 | Halonen et al. |
| 20040120472 | June 24, 2004 | Popay et al. |
| 20040120476 | June 24, 2004 | Harrison et al. |
| 20040138890 | July 15, 2004 | Ferrans et al. |
| 20040153323 | August 5, 2004 | Charney et al. |
| 20040179038 | September 16, 2004 | Blattner et al. |
| 20040215453 | October 28, 2004 | Orbach |
| 20040216036 | October 28, 2004 | Chu et al. |
| 20040236574 | November 25, 2004 | Ativanichayaphong et al. |
| 20040260562 | December 23, 2004 | Kujirai |
| 20050060158 | March 17, 2005 | Endo et al. |
| 20050075884 | April 7, 2005 | Badt |
| 20050091059 | April 28, 2005 | Lecoeuche |
| 20050131701 | June 16, 2005 | Cross et al. |
| 20050138219 | June 23, 2005 | Bou-Ghannam et al. |
| 20050138647 | June 23, 2005 | Bou-ghannam et al. |
| 20050154580 | July 14, 2005 | Horowitz et al. |
| 20050160461 | July 21, 2005 | Baumgartner et al. |
| 20050188412 | August 25, 2005 | Dacosta |
| 20050203729 | September 15, 2005 | Roth et al. |
| 20050203747 | September 15, 2005 | Lecoeuche |
| 20050261908 | November 24, 2005 | Cross et al. |
| 20050273769 | December 8, 2005 | Eichenberger et al. |
| 20050283367 | December 22, 2005 | Ativanichayaphong et al. |
| 20060047510 | March 2, 2006 | Ativanichayaphong et al. |
| 20060064302 | March 23, 2006 | Ativanichayaphong et al. |
| 20060069564 | March 30, 2006 | Allison et al. |
| 20060074680 | April 6, 2006 | Cross et al. |
| 20060075120 | April 6, 2006 | Smit |
| 20060111906 | May 25, 2006 | Cross et al. |
| 20060122836 | June 8, 2006 | Cross et al. |
| 20060123358 | June 8, 2006 | Lee et al. |
| 20060136222 | June 22, 2006 | Cross et al. |
| 20060146728 | July 6, 2006 | Engelsma et al. |
| 20060168095 | July 27, 2006 | Sharma et al. |
| 20060168595 | July 27, 2006 | McArdle |
| 20060184626 | August 17, 2006 | Agapi et al. |
| 20060190264 | August 24, 2006 | Jaramillo et al. |
| 20060218039 | September 28, 2006 | Johnson |
| 20060229880 | October 12, 2006 | White et al. |
| 20060235694 | October 19, 2006 | Cross et al. |
| 20060287845 | December 21, 2006 | Cross et al. |
| 20060287865 | December 21, 2006 | Cross et al. |
| 20060287866 | December 21, 2006 | Cross et al. |
| 20060288309 | December 21, 2006 | Cross et al. |
| 20070265851 | November 15, 2007 | Ben-David et al. |
| 20070274296 | November 29, 2007 | Cross et al. |
| 20070274297 | November 29, 2007 | Cross et al. |
| 20070288241 | December 13, 2007 | Cross et al. |
| 20070294084 | December 20, 2007 | Cross et al. |
| 20080065386 | March 13, 2008 | Cross et al. |
| 20080065387 | March 13, 2008 | Cross, Jr. et al. |
| 20080065388 | March 13, 2008 | Cross et al. |
| 20080065389 | March 13, 2008 | Cross et al. |
| 20080065390 | March 13, 2008 | Ativanichayaphong et al. |
| 20080086564 | April 10, 2008 | Putman et al. |
| 20080140410 | June 12, 2008 | Ativanichayaphong et al. |
| 20080162136 | July 3, 2008 | Agapi et al. |
| 20080177530 | July 24, 2008 | Cross et al. |
| 20080195393 | August 14, 2008 | Cross et al. |
| 20080208584 | August 28, 2008 | Ativanichayaphong et al. |
| 20080208585 | August 28, 2008 | Ativanichayaphong et al. |
| 20080208586 | August 28, 2008 | Ativanichayaphong et al. |
| 20080208587 | August 28, 2008 | Ben-David et al. |
| 20080208588 | August 28, 2008 | Ativanichayaphong et al. |
| 20080208589 | August 28, 2008 | Cross et al. |
| 20080208590 | August 28, 2008 | Cross et al. |
| 20080208591 | August 28, 2008 | Ativanichayaphong et al. |
| 20080208592 | August 28, 2008 | Cross et al. |
| 20080208593 | August 28, 2008 | Ativanichayaphong et al. |
| 20080208594 | August 28, 2008 | Cross et al. |
| 20080228494 | September 18, 2008 | Cross |
| 20080228495 | September 18, 2008 | Cross, Jr. et al. |
| 20080235021 | September 25, 2008 | Cross et al. |
| 20080235022 | September 25, 2008 | Bergl et al. |
| 20080235027 | September 25, 2008 | Cross |
| 20080235029 | September 25, 2008 | Cross et al. |
| 20080249782 | October 9, 2008 | Ativanichayaphong et al. |
| 20080255850 | October 16, 2008 | Cross et al. |
| 20080255851 | October 16, 2008 | Ativanichayaphong et al. |
| 1385783 | December 2002 | CN |
| 1564123 | January 2005 | CN |
| 0 794 670 | September 1997 | EP |
| 1 450 350 | August 2004 | EP |
| 0507148.5 | April 2005 | GB |
| 2000155529 | June 2000 | JP |
| 2003140672 | May 2003 | JP |
| WO 99/48088 | September 1999 | WO |
| WO 00/51106 | August 2000 | WO |
| WO 02/32140 | April 2002 | WO |
| WO 2004/062945 | July 2004 | WO |
| WO 2006/108795 | October 2006 | WO |
- Axelsson, et al.; “XHTML+Voice Profile 1.2” Internet, [Online] Mar. 16, 2004, pp. 1-53, XP002484188 Retrieved from theInternet: URL:http://www.voicexml.org/specs/mutlimodal/x+v/12/spec.html [retrieved on Jun. 12, 2008].
- W3C: “Voice Extensible Markup Language (VoiceXML) Version 2.0” Internet Citation, [Online] XP002248286 Retrieved from the Internet: URL:http://www.w3.org/TR/voicexml20 [retrieved on Jul. 18, 2003].
- W3C: “Voice Extensible Markup Language (VoiceXML) 2.1, W3C Candidate Recommendation Jun. 13, 2005” Internet, [Online] Jun. 13, 2005, pp. 1-34, XP002484189 Retrieved from the Internet: URL:http://www.w3.org/TR/2005/CR-voicexml21-20050613/ [retrievedon Jun. 12, 2008].
- PCT Search Report, Jun. 25, 2008; PCT Application No. PCT/EP2008/051358.
- PCT Search Report, Jun. 18, 2008; PCT Application No. PCT/EP2008/051363.
- Didier Guillevic, et al.,Robust Semantic Confidence Scoring ICSLP 2002: 7.sup.th International Conference on Spoken Language Processing. Denver Colorado, Sep. 16-20, 2002 International Conference on Spoken Language Processing (ICSLP), Adelaide:Causal Productions, AI, Sep. 16, 2002, p. 853, XP007011561 ISBN:9788-1-876346-40-9.
- U.S. Appl. No. 10/919,005, filed Dec. 2005, Eichenberger, et al.
- U.S. Appl. No. 12/109,151, filed Apr. 2008, Agapi, et al.
- U.S. Appl. No. 12/109,167, filed Apr. 2008, Agapi, et al.
- U.S. Appl. No. 12/109,204, filed Apr. 2008, Agapi, et al.
- U.S. Appl. No. 12/109,227, filed Apr. 2008, Agapi, et al.
- U.S. Appl. No. 12/109,214, filed Apr. 2008, Agapi, et al.
Type: Grant
Filed: Nov 26, 2013
Date of Patent: May 17, 2016
Patent Publication Number: 20140122091
Assignee: Nuance Communications, Inc. (Burlington, MA)
Inventors: Charles W. Cross, Jr. (Wellington, FL), Hilary A. Pike (Boca Raton, FL)
Primary Examiner: Shaun Roberts
Application Number: 14/090,354
International Classification: G10L 21/00 (20130101); G10L 15/22 (20060101); G10L 17/00 (20130101); G10L 13/00 (20060101); H04M 3/493 (20060101);
